1. Field of the Invention
The present invention relates to methods for characterizing petroleum fluids extracted from a hydrocarbon bearing geological formation. The invention has application to reservoir architecture understanding, although it is not limited thereto.
2. Description of Related Art
Petroleum consists of a complex mixture of hydrocarbons of various molecular weights, plus other organic compounds. The exact molecular composition of petroleum varies widely from formation to formation. The proportion of hydrocarbons in the mixture is highly variable and ranges from as much as 97% by weight in the lighter oils to as little as 50% in the heavier oils and bitumens. The hydrocarbons in petroleum are mostly alkanes (linear or branched), cycloalkanes, aromatic hydrocarbons, or more complicated chemicals like asphaltenes. The other organic compounds in petroleum typically contain carbon dioxide (CO2), nitrogen, oxygen, and sulfur, and trace amounts of metals such as iron, nickel, copper, and vanadium.
The alkanes, also known as paraffins, are saturated hydrocarbons with straight or branched chains which contain only carbon and hydrogen and have the general formula CnH2n+2. They generally have from 5 to 40 carbon atoms per molecule, although trace amounts of shorter or longer molecules may be present in the mixture. The alkanes include methane (CH4), ethane (C2H6), propane (C3H8), i-butane (iC4H10), n-butane (nC4H10), i-pentane (iC5H12), n-pentane (nC5H12), hexane (C6H14), heptane (C7H16), octane (C8H18), nonane (C9H20), decane (C10H22), hendecane (C11H24)—also referred to as endecane or undecane, dodecane (C12H26), tridecane (C13H28), tetradecane (C14H30), pentadecane (C15H32), and hexadecane (C16H34).
The cycloalkanes, also known as napthenes, are saturated hydrocarbons which have one or more carbon rings to which hydrogen atoms are attached according to the formula CnH2n. Cycloalkanes have similar properties to alkanes, but have higher boiling points. The cycloalkanes include cyclopropane (C3H6), cyclobutane (C4H8), cyclopentane (C5H10), cyclohexane (C6H12), cycloheptane (C7H14), etc.
The aromatic hydrocarbons are unsaturated hydrocarbons which have one or more planar six-carbon rings called benzene rings, to which hydrogen atoms are attached with the formula CnHn. They tend to burn with a sooty flame, and many have a sweet aroma. Some are carcinogenic. The aromatic hydrocarbons include benzene (C6H6) and derivatives of benzene, and polyaromatic hydrocarbons.
Asphaltenes consist primarily of carbon, hydrogen, nitrogen, oxygen, and sulfur, as well as trace amounts of vanadium and nickel. The C:H ratio is approximately 1:1.2, depending on the asphaltene source. Asphaltenes have been shown to have a distribution of molecular masses in the range of 400 g/mol to 1500 g/mol with a maximum around 750 g/mol. The chemical structure of asphaltene is difficult to ascertain due to its complex nature, but has been studied by existing techniques. It is undisputed that asphaltene is composed mainly of polyaromatic carbon, i.e. polycondensed aromatic benzene units with nitrogen, sulfur, and oxygen (NSO-compounds) combined with minor amounts of a series of heavy metals, particularly vanadium and nickel, which occur in porphyrin structures. Asphaltenes are today widely recognized as soluble, chemically altered fragments of kerogen which migrated out of the source rock for the oil, during oil catagenesis. Asphaltenes are dispersed in reservoir petroleum fluid as nanoaggregates with ˜4-10 monomers and ˜2-3 nanometers in diameter. Heavy oils and tar sands contain much higher proportions of asphaltenes than do medium-API oils or light oils. Condensates are virtually devoid of asphaltenes.
Computer-based modeling and simulation techniques have been developed for estimating the properties and/or behavior of petroleum fluid in a reservoir of interest. Typically, such techniques employ an equation of state (EOS) model that represents the phase behavior of the petroleum fluid in the reservoir. Once the EOS model is defined, it can be used to compute a wide array of properties of the petroleum fluid of the reservoir, such as gas-oil ratio (GOR) or condensate-gas ratio (CGR), density of each phase, volumetric factors and compressibility, and heat capacity and saturation pressure (bubble or dew point). Thus, the EOS model can be solved to obtain saturation pressure at a given temperature. Moreover, GOR, CGR, phase densities, and volumetric factors are byproducts of the EOS model. Transport properties, such as heat capacity or viscosity, can be derived from properties obtained from the EOS model, such as fluid composition. Furthermore, the EOS model can be extended with other reservoir evaluation techniques for compositional simulation of flow and production behavior of the petroleum fluid of the reservoir, as is well know in the art. For example, compositional simulations can be helpful in studying (1) depletion of a volatile oil or gas condensate reservoir where phase compositions and properties vary significantly with pressure below bubble or dew point pressures, (2) injection of non-equilibrium gas (dry or enriched) into a black oil reservoir to mobilize oil by vaporization into a more mobile gas phase or by condensation through an outright (single-contact) or dynamic (multiple-contact) miscibility, and (3) injection of CO2 into an oil reservoir to mobilize oil by miscible displacement and by oil viscosity reduction and oil swelling.
In the past, fluid homogeneity in a hydrocarbon reservoir has been assumed. However, there is now a growing awareness that fluids are often heterogeneous or compartmentalized in the reservoir. A compartmentalized reservoir consists of two or more compartments that effectively are not in hydraulic communication. Two types of reservoir compartmentalization have been identified, namely vertical and lateral compartmentalization. Lateral compartmentalization usually occurs as a result of faulting or stratigraphic changes in the reservoir, while vertical compartmentalization results from sealing barriers, such as shales.
Molecular and thermal diffusion, natural convection, biodegradation, adsorption, and external fluxes can also lead to non-equilibrium hydrocarbon distribution in a reservoir.
Reservoir compartmentalization, as well as non-equilibrium hydrocarbon distribution, can significantly hinder production and can make the difference between an economically viable field and an economically nonviable field. Techniques to aid an operator to accurately describe reservoir compartments and their distribution, as well as non-equilibrium hydrocarbon distribution, can increase understanding of such reservoirs and ultimately raise production.
Conventionally, reservoir architecture (i.e., reservoir compartmentalization as well as non-equilibrium hydrocarbon distribution) has been determined using pressure-depth plots and pressure gradient analysis with traditional straight-line regression schemes. This process may, however, be misleading as fluid compositional changes and compartmentalization give distortions in the pressure gradients, which result in erroneous interpretations of fluid contacts or pressure seals. Additionally, pressure communication does not prove flow connectivity because pressure communication is a necessary, but insufficient, condition to establish flow connectivity at production scales.
US Patent Application Publication No. 2009/0248310 provides a methodology for determining reservoir architecture employing downhole fluid analysis in conjunction with EOS models that estimate gradients of a number of compositional components in a reservoir as a function of depth due to gravitational forces, chemical forces, and thermal diffusion. Particularly, an estimate of an asphaltene component (i.e., mass fraction of n-heptane insoluble asphaltene) is derived from the EOS model and used in conjunction with an empirical correlation between the asphaltene component estimate and optical absorption measurement data to make a determination related to reservoir architecture.
In some instances, it can be difficult to derive a model that accurately reflects compositional components (particularly, asphaltene components) in a reservoir as a function of depth. Such difficulties can be compounded in the event that the reservoir fluids consist of a large amount of asphaltenes and/or asphaltenes are unstable under reservoir conditions and/or clusters of asphaltene nanoaggregates exist in the reservoir. The clusters of asphaltene nanoaggregates have a much larger diameter that the common asphaltene nanoaggregates and are suspended in the reservoir fluid. In all of these cases, the asphaltene gradient can be significant. As a result, oil density and viscosity gradients can be considerable as well. In these circumstances, accurately characterizing asphaltenes is difficult and it can become necessary to acquire and analyze more downhole samples in order to refine or tune the compositional analysis based thereon. Moreover, it is often difficult to assess the accuracy of the compositional analysis at any given time, and thus know whether or not there is a need to acquire and analyze more downhole samples in order to refine or tune the compositional analysis.